Paper Citation Record · LEDGER

Learning Rarefied Gas Dynamics with Physics-Enforced Neural Networks

As of 5 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 0 inbound Pith citation observations for arXiv:2509.06231.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.06231 v1

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T23:56:21.084296Z

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

6 of 6 outbound references displayed

verified exact2
verified fuzzy1
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7af18856-a409-4a4d-9054-f4723fc7431b · outbound

This paper cites Springer Nature Singapore.

Learning Rarefied Gas Dynamics with Physics-Enforced Neural Networks Springer Nature Singapore

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T23:56:20.938168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d90f7e8-747b-49c8-89ad-6dbe96ccddb7 · outbound

This paper cites Data -Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks.

Learning Rarefied Gas Dynamics with Physics-Enforced Neural Networks Data -Driven Surrogate Modeling of DSMC Solutions Using Deep Neural Networks

Reference 3

Resolution: metadata mismatch; raw_fallback, observed 2026-08-04T23:56:21.688314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 89b27bdf-af2c-485b-b9ff-43a2fb29ab3b · outbound

This paper cites Fine -Tuning Physics-Informed Neural Networks for Cavity Flows Using Coordinate Transformation.

Learning Rarefied Gas Dynamics with Physics-Enforced Neural Networks Fine -Tuning Physics-Informed Neural Networks for Cavity Flows Using Coordinate Transformation

Reference 4

Resolution: verified exact; raw_fallback, observed 2026-08-04T23:56:21.484931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 22c218e0-af2d-42ff-80f1-811ee122f768 · outbound

This paper cites Solving Continuum and Rarefied Flows Using Differentiable Programming.

Learning Rarefied Gas Dynamics with Physics-Enforced Neural Networks Solving Continuum and Rarefied Flows Using Differentiable Programming

Reference 5

Resolution: verified exact; raw_fallback, observed 2026-08-04T23:56:21.318738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6f689a22-264f-4099-b3d6-9a649017f62b · outbound

This paper cites Towards efficient simulations of non - equilibrium chemistry in hypersonic flows: neural operator -enhanced 1 -D shock simulations.

Learning Rarefied Gas Dynamics with Physics-Enforced Neural Networks Towards efficient simulations of non - equilibrium chemistry in hypersonic flows: neural operator -enhanced 1 -D shock simulations

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:56:21.817414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7b286456-d2e3-4f9a-a76f-a00f232b2a14 · outbound

This paper cites Fusion-DeepONet: A Data-Efficient Neural Operator for Geometry-Dependent Hypersonic and Supersonic Flows.

Learning Rarefied Gas Dynamics with Physics-Enforced Neural Networks Fusion-DeepONet: A Data-Efficient Neural Operator for Geometry-Dependent Hypersonic and Supersonic Flows

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-04T23:56:20.898864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.